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Showing result 1 - 5 of 13 essays matching the above criteria.

  1. 1. Modulating Depth Map Features to Estimate 3D Human Pose via Multi-Task Variational Autoencoders

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Kobe Moerman; [2023]
    Keywords : 3D pose estimation; Joint landmarks; Variational autoencoder; Multi-task model; Loss discrimination; Latent-space modulation; Depth map; 3D-positionsuppskattning; Gemensamma landmärken; Variationell autoencoder; Multitask-modell; Förlustdiskriminering; Latent-space-modulering; Djupkarta;

    Abstract : Human pose estimation (HPE) constitutes a fundamental problem within the domain of computer vision, finding applications in diverse fields like motion analysis and human-computer interaction. This paper introduces innovative methodologies aimed at enhancing the accuracy and robustness of 3D joint estimation. READ MORE

  3. 2. Multi-Scale Task Dynamics in Transfer and Multi-Task Learning : Towards Efficient Perception for Autonomous Driving

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Simon Ekman von Huth; [2023]
    Keywords : Autonomous Driving; Computer Vision; Deep Learning; Machine Learning; Multi-Task Learning; Transfer Learning; Task Relationships; Task Dynamics; Python; Multi-Scale Representation Learning; Fuss-Free Network; Självkörande Fordon; Datorseende; Djupinlärning; Maskininlärning; Multiuppgiftsinlärning; Överföringsinlärning; Uppgiftsrelationer; Uppgiftsdynamik; Python; Flerskalig Representationsinlärning; Fuss-Free Nätverk;

    Abstract : Autonomous driving technology has the potential to revolutionize the way we think about transportation and its impact on society. Perceiving the environment is a key aspect of autonomous driving, which involves multiple computer vision tasks. READ MORE

  4. 3. Remembering how to walk - Using Active Dendrite Networks to Drive Physical Animations

    University essay from Umeå universitet/Institutionen för fysik

    Author : Klas Henriksson; [2023]
    Keywords : reinforcement learning; deep learning; physical animation; deep reinforcement learning; multi-task learning; multi-task reinforcement learning; machine learning; neural networks;

    Abstract : Creating embodied agents capable of performing a wide range of tasks in different types of environments has been a longstanding challenge in deep reinforcement learning. A novel network architecture introduced in 2021 called the Active Dendrite Network [A. Iyer et al. READ MORE

  5. 4. Attention-based Multi-Behavior Sequential Network for E-commerce Recommendation

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Zilong Li; [2022]
    Keywords : Recommendation System; Sequential Recommendation; Click-through Rate Model; Transformer; Multi-Task Learning; Sistema di Raccomandazione; Raccomandazione Sequenziale; Modello di Percentuale di Clic; Trasformatore; Apprendimento Multitasking; Rekommendationssystem; Sekventiell rekommendation; Klickfrekvensmodell; Transformator; Multi-Task Learning;

    Abstract : The original intention of the recommender system is to solve the problem of information explosion, hoping to help users find the content they need more efficiently. In an e-commerce platform, users typically interact with items that they are interested in or need in a variety of ways. For example, buying, browsing details, etc. READ MORE

  6. 5. Multi-task regression QSAR/QSPR prediction utilizing text-based Transformer Neural Network and single-task using feature-based models

    University essay from Linköpings universitet/Statistik och maskininlärning

    Author : Spyridon Dimitriadis; [2021]
    Keywords : multi-task regression; QSAR; QSPR; deep learning; attention based models; transfer learning;

    Abstract : With the recent advantages of machine learning in cheminformatics, the drug discovery process has been accelerated; providing a high impact in the field of medicine and public health. Molecular property and activity prediction are key elements in the early stages of drug discovery by helping prioritize the experiments and reduce the experimental work. READ MORE